


'''simple docstring'''
import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def       _lowercase							(					UpperCamelCase__							:       Optional[Any],					UpperCamelCase__							:       Optional[int]      ):
 __A	:		Any	  =      args.log_outputs
 __A	:		int	  =      '_'.join(args.dataset.split('/'      ) + [args.config, args.split]      )

 # load metric
 __A	:		Union[str, Any]	  =      load_metric('wer'      )
 __A	:		Dict	  =      load_metric('cer'      )

 # compute metrics
 __A	:		int	  =      wer.compute(references=result['target'],					predictions=result['prediction']      )
 __A	:		Union[str, Any]	  =      cer.compute(references=result['target'],					predictions=result['prediction']      )

 # print & log results
 __A	:		Union[str, Any]	  =      f"""WER: {wer_result}\nCER: {cer_result}"""
 print(UpperCamelCase__      )

 with open(f"""{dataset_id}_eval_results.txt""",					'w'      ) as f:
  f.write(UpperCamelCase__      )

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  __A	:		List[str]	  =      f"""log_{dataset_id}_predictions.txt"""
  __A	:		Optional[Any]	  =      f"""log_{dataset_id}_targets.txt"""

  with open(UpperCamelCase__,					'w'      ) as p, open(UpperCamelCase__,					'w'      ) as t:
   # mapping function to write output
   def write_to_file(UpperCamelCase__							:       Union[str, Any],					UpperCamelCase__							:       str      ):
    p.write(f"""{i}""" + '\n'      )
    p.write(batch['prediction'] + '\n'      )
    t.write(f"""{i}""" + '\n'      )
    t.write(batch['target'] + '\n'      )

   result.map(UpperCamelCase__,					with_indices=UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       str      ):
 __A	:		Any	  =      '[,?.!\-\;\:\"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 __A	:		Tuple	  =      re.sub(UpperCamelCase__,					'',					text.lower()      )

 # In addition, we can normalize the target text, e.g. removing new lines characters etc...
 # note that order is important here!
 __A	:		Union[str, Any]	  =      ['\n\n', '\n', '   ', '  ']

 for t in token_sequences_to_ignore:
  __A	:		List[Any]	  =      ' '.join(text.split(UpperCamelCase__      )      )

 return text


def       _lowercase							(					UpperCamelCase__							:       Tuple      ):
 # load dataset
 __A	:		str	  =      load_dataset(args.dataset,					args.config,					split=args.split,					use_auth_token=UpperCamelCase__      )

 # for testing: only process the first two examples as a test
 # dataset = dataset.select(range(10))

 # load processor
 __A	:		str	  =      AutoFeatureExtractor.from_pretrained(args.model_id      )
 __A	:		Union[str, Any]	  =      feature_extractor.sampling_rate

 # resample audio
 __A	:		List[Any]	  =      dataset.cast_column('audio',					Audio(sampling_rate=UpperCamelCase__      )      )

 # load eval pipeline
 if args.device is None:
  __A	:		Optional[int]	  =      0 if torch.cuda.is_available() else -1
 __A	:		int	  =      pipeline('automatic-speech-recognition',					model=args.model_id,					device=args.device      )

 # map function to decode audio
 def map_to_pred(UpperCamelCase__							:       List[str]      ):
  __A	:		Optional[int]	  =      asr(
      batch['audio']['array'],					chunk_length_s=args.chunk_length_s,					stride_length_s=args.stride_length_s      )

  __A	:		Optional[Any]	  =      prediction['text']
  __A	:		str	  =      normalize_text(batch['sentence']      )
  return batch

 # run inference on all examples
 __A	:		List[Any]	  =      dataset.map(UpperCamelCase__,					remove_columns=dataset.column_names      )

 # compute and log_results
 # do not change function below
 log_results(UpperCamelCase__,					UpperCamelCase__      )


if __name__ == "__main__":
 UpperCAmelCase_			:  Dict										=   argparse.ArgumentParser()

 parser.add_argument(
     '--model_id', type=str, required=True, help='Model identifier. Should be loadable with 🤗 Transformers'
 )
 parser.add_argument(
     '--dataset',
     type=str,
     required=True,
     help='Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets',
 )
 parser.add_argument(
     '--config', type=str, required=True, help='Config of the dataset. *E.g.* `\'en\'`  for Common Voice'
 )
 parser.add_argument('--split', type=str, required=True, help='Split of the dataset. *E.g.* `\'test\'`')
 parser.add_argument(
     '--chunk_length_s', type=float, default=None, help='Chunk length in seconds. Defaults to 5 seconds.'
 )
 parser.add_argument(
     '--stride_length_s', type=float, default=None, help='Stride of the audio chunks. Defaults to 1 second.'
 )
 parser.add_argument(
     '--log_outputs', action='store_true', help='If defined, write outputs to log file for analysis.'
 )
 parser.add_argument(
     '--device',
     type=int,
     default=None,
     help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.',
 )
 UpperCAmelCase_			:  int										=   parser.parse_args()

 main(args)







"""simple docstring"""



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


SCREAMING_SNAKE_CASE__:Optional[Any]									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__()
        __a       =    nn.ModuleList(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = True    ,		):
        for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase    ,		lowerCamelCase    ,		self.nets						)						):
               __a ,   __a       =    controlnet(
                   lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		)

               # merge samples
               if i == 0:
                      __a ,   __a       =    down_samples, mid_sample
               else:
                      __a       =    [
                          samples_prev + samples_curr
                          for samples_prev, samples_curr in zip(lowerCamelCase    ,		lowerCamelCase						)
                      ]
                      mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		):
        __a       =    0
        __a       =    save_directory
        for controlnet in self.nets:
               controlnet.save_pretrained(
                   lowerCamelCase    ,		is_main_process=lowerCamelCase    ,		save_function=lowerCamelCase    ,		safe_serialization=lowerCamelCase    ,		variant=lowerCamelCase    ,		)

               idx += 1
               __a       =    model_path_to_save + F"_{idx}"






 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    0
        __a       =    []

        # load controlnet and append to list until no controlnet directory exists anymore
        # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
        # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
        __a       =    pretrained_model_path
        while os.path.isdir(lowerCamelCase						):
               __a       =    ControlNetModel.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)
               controlnets.append(lowerCamelCase						)

               idx += 1
               __a       =    pretrained_model_path + F"_{idx}"

        logger.info(F"{len(lowerCamelCase						)} controlnets loaded from {pretrained_model_path}."						)

        if len(lowerCamelCase						) == 0:
               raise ValueError(
                   F"No ControlNets found under {os.path.dirname(lowerCamelCase						)}. Expected at least {pretrained_model_path + '_0'}."						)

        return cls(lowerCamelCase						)
